MGMA Webinar: Reinventing the Revenue Cycle with Ai, Analytics, & New Benchmarks
Healthcare revenue is bleeding margin. Learn how AI, analytics & ruthless performance benchmarks can stop the waste before system flatline.
Current stats related to U.S. administrative waste in healthcare are alarming, equating to about 30% of total spending waste. In response, healthcare organizations are prioritizing adoption of AI and automation to improve the outlook. This is a step in the right direction, but many, unfortunately, are leaving money on the table because they lack visibility into human generated data.
Knowledge of exactly where the breakdown occurred in the revenue cycle that required human intervention for claims adjudication allows healthcare organizations to quickly diagnose how to fix the problem. Organizations that achieve this kind of visibility into daily work effort will be armed with the information needed to support three proven strategies for improving bottom-line impact.
Across millions of human touches related to collecting on insurance claims within MedEvolve’s database, we find that upwards of 50% of touches are wasted on a consistent basis—and every touch negatively impacts margin. Reeling in that waste can mean hundreds of thousands, if not millions, of dollars that can be recouped to improve margin. Learn more about how to prevent wasted claim touches.
On any given day, 80-90% of open claims do not require any work yet legacy workflow solutions are incapable of identifying and carving out those claims. One way to improve staff efficiency and effectiveness is to prioritize the claims that need attention and measure the effectiveness of staff once they take action on a claim. Learn about how Atlas Healthcare Partners increased labor capacity by 31%.
With access to the human generated data through a combination of workflow automation and generative AI, healthcare leaders can determine when and where changes are occurring in the revenue cycle and understand the true status of claims, the action taken by staff and the team’s ability to positively impact the AR balance. Learn more about how to leverage human-generated data in the revenue cycle.
Effective Intelligence combines Patient Financial Clearance Automation, Medical Billing Workflow Automation, and Real-Time RCM Analytics in a cloud-based platform designed to integration with your current EMR/PM technology to measure the effectiveness of your RCM staff.
Review and assess your practice’s financial status in 5 min or less and know exact where you are losing money and why. Measure the work effort of every revenue cycle employee, incentivize and retain your top performers, and help employees that need improvement.
You can prevent most common denials, rejections and write-offs during the scheduling and pre-registration process in advance of the appointment. Configure checkpoints and use central task management to quickly clear patients and keep your front office staffing needs at a minimum.
As team members log in to the web-based application and record each “touch” of a claim, outcome, and next task, key data points are recorded like who completed the task and when, outcome, task notes, internal / external messages sent, collection success and other data points that feed into our real-time analytics.
Reduce RCM labor dependence with financial clearance, insurance A/R, & patient A/R automation modules with real-time analytics.
Increase productivity and simplify front & back office processes while keeping your staff focused with our flagship PM system.
Healthcare revenue is bleeding margin. Learn how AI, analytics & ruthless performance benchmarks can stop the waste before system flatline.
Under OBBBA, you as a healthcare executive are being forced to change “the way we’ve always done it.” With more uninsured patients seeking medical help and fewer federal dollars to
In this episode of Disruption/Interruption, Matt shares how generative AI and a focus on human accountability can help providers achieve “zero touch” claims, reduce waste, and improve access to care,
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